The current synthetic aperture radar (SAR) images with ultrahigh-resolution provide detailed structures of the urban areas. Utilizing stacks of ultrahigh-resolution SAR images acquired with different view angles, tomographic SAR (TomoSAR) becomes an advanced technique to retrieve 3-D spatial information of the detailed structures which presents the efficient density in the point clouds. The technique is a sparse reconstruction problem indeed and can be solved by compressive sensing (CS) algorithms. However, conventional CS algorithms process the pixel independently and the detailed structures of the targets are easily lost followed by the sparsity constraints. In this article, we apply morphology regularization as a prior term to form a novel approach based on the CS algorithm. The morphology regularization enhances the detailed 3-D structural properties of targets, which can shrink the concatenations caused by outliers in the iterative reconstruction. As for the optimization algorithm for tomographic inversion, we apply the framework of the alternating direction method of multipliers (ADMMs), where the Bregman iteration is adopted for solving the subproblem with morphology regularization. Both simulation experiments and tests on real data show that the proposed method can suppress the outliers and guarantee the detection rate, leading to excellent correctness and completeness.

中文翻译：

---

城市地区超高分辨率 SAR 图像 TomoSAR 形态正则化


当前的超高分辨率合成孔径雷达（SAR）图像提供了城市地区的详细结构。利用不同视角采集的超高分辨率 SAR 图像堆栈，层析 SAR (TomoSAR) 成为检索详细结构的 3D 空间信息的先进技术，该信息呈现点云中的有效密度。该技术确实是一个稀疏重建问题，可以通过压缩感知（CS）算法来解决。然而，传统的CS算法独立地处理像素，并且由于稀疏性约束而容易丢失目标的详细结构。在本文中，我们应用形态学正则化作为先验术语，形成一种基于 CS 算法的新颖方法。形态正则化增强了目标的详细 3D 结构特性，可以缩小迭代重建中异常值引起的级联。对于层析反演的优化算法，我们采用乘子交替方向法（ADMM）的框架，其中采用Bregman迭代来求解形态学正则化子问题。仿真实验和真实数据测试表明，该方法能够抑制异常值，保证检测率，具有良好的正确性和完整性。